Multifocus Image Fusion Using Biogeography-Based Optimization

Mathematical Problems in Engineering, Feb 2015

For multifocus image fusion in spatial domain, sharper blocks from different source images are selected to fuse a new image. Block size significantly affects the fusion results and a fixed block size is not applicable in various multifocus images. In this paper, a novel multifocus image fusion algorithm using biogeography-based optimization is proposed to obtain the optimal block size. The sharper blocks of each source image are first selected by sum modified Laplacian and morphological filter to contain an initial fused image. Then, the proposed algorithm uses the migration and mutation operation of biogeography-based optimization to search the optimal block size according to the fitness function in respect of spatial frequency. The chaotic search is adopted during iteration to improve optimization precision. The final fused image is constructed based on the optimal block size. Experimental results demonstrate that the proposed algorithm has good quantitative and visual evaluations.

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Multifocus Image Fusion Using Biogeography-Based Optimization

Multifocus Image Fusion Using Biogeography-Based Optimization Ping Zhang,1 Chun Fei,2 Zhenming Peng,1 Jianping Li,2 and Hongyi Fan3 1School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu 611731, China 2School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 3School of Engineering, Brown University, Providence, RI 02912, USA Received 11 October 2014; Revised 4 February 2015; Accepted 7 February 2015 Academic Editor: George S. Dulikravich Copyright © 2015 Ping Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract For multifocus image fusion in spatial domain, sharper blocks from different source images are selected to fuse a new image. Block size significantly affects the fusion results and a fixed block size is not applicable in various multifocus images. In this paper, a novel multifocus image fusion algorithm using biogeography-based optimization is proposed to obtain the optimal block size. The sharper blocks of each source image are first selected by sum modified Laplacian and morphological filter to contain an initial fused image. Then, the proposed algorithm uses the migration and mutation operation of biogeography-based optimization to search the optimal block size according to the fitness function in respect of spatial frequency. The chaotic search is adopted during iteration to improve optimization precision. The final fused image is constructed based on the optimal block size. Experimental results demonstrate that the proposed algorithm has good quantitative and visual evaluations. 1. Introduction Optical lenses with long focal lengths often suffer from the problem of limited depth of field. It is impossible to get an image that contains all relevant objects in focus. The objects only on the focus plane are sharpness and other objects in front of or behind the focus plane are blurred [1]. Multifocus fusion method which synthesizes multiple images of the same view point under different focal settings can be used to extend depth of field and obtain an all-in focus image. The fused image has more useful information of the view point and is more suitable for many applications than any individual images. Multifocus image fusion technology has played important roles in many fields such as target recognition, remote sensing, medical diagnosis, and military application [2]. Many multifocus fusion algorithms have been proposed in recent years. Basically, these fusion algorithms can be categorized into two groups: spatial domain fusion and transform domain fusion [3]. For spatial domain fusion, a new image is fused by directly selecting different regions from source images. Firstly, source images are divided into nonoverlapping blocks. Then, the sharpness values of blocks are calculated based on different sharpness measure methods. Finally, the sharper blocks from different source images are selected to fuse a new image. The common algorithms in spatial domain include average, variance, energy of image gradient (EOG), sum modified Laplacian (SML), and spatial frequency (SF) [4]. Recently, many new algorithms in spatial domain have been proposed to improve efficiency, such as artificial neural network (ANN) [5], pulse coupled neural network (PCNN) [6], independent component analysis (ICA) [7], robust principal component analysis (RPCA) [8], and neighbor distance [9]. For transform domain fusion, a new image is generated with certain frequency transforms. Firstly, source images are converted into a transform domain to obtain the corresponding transform coefficients. Then, the transform coefficients are integrated together based on different fusion rules. Finally, the fused image is constructed by applying the inverse transform. The common algorithms in transform domain are based on pyramid transform, such as Laplacian pyramid (LAP), gradient pyramid (GRP), ratio of low-pass pyramid (RAP), and morphological pyramid (ROP) [10]. Some fusion algorithms based on wavelet transform are proposed which are generally superior to the fusion algorithms based on pyramid transform, such as discrete wavelet transform (DWT) [11], stationary wavelet transform (SWT) [12], and dual-tree complex wavelet transform (DTCWT) [13]. Recently, many new multiscale multiresolution transform algorithms have been widely proposed in image fusion, such as curvelet transform [14], contourlet transform [15], nonsubsampled contourlet transform (NSCT) [16], and nonsubsampled shearlet transform (NSST) [17]. The transform domain algorithms have no block artifact, but they usually are complicated and time-consuming to implement. And the multiscale multiresolution transform algorithms are generally shift-variant and sensitive to noise. The spatial (...truncated)


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Ping Zhang, Chun Fei, Zhenming Peng, Jianping Li, Hongyi Fan. Multifocus Image Fusion Using Biogeography-Based Optimization, Mathematical Problems in Engineering, 2015, 2015, DOI: 10.1155/2015/340675